{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8cf17cd",
   "metadata": {},
   "source": [
    "# AidSDK部署-ImageNet1000-摄像头和视频-中文\n",
    "\n",
    "同济子豪兄 2023-6-25\n",
    "\n",
    "本代码在AidLux手机端运行"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52da8508",
   "metadata": {},
   "source": [
    "## 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf60b574",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "from cvs import *\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from PIL import Image, ImageFont, ImageDraw\n",
    "\n",
    "import aidlite_gpu\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79e59c3",
   "metadata": {},
   "source": [
    "## 导入中文字体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2e2dd9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入中文字体，指定字体大小\n",
    "font = ImageFont.truetype('SimHei.ttf', 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9c4236",
   "metadata": {},
   "source": [
    "## 载入类别名称与ID映射表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3ad474d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 中文类别名称\n",
    "idx_to_labels = np.load('imagenet1000_idx_to_labels_zh.npy', allow_pickle=True).item()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b4e1b0",
   "metadata": {},
   "source": [
    "## 载入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa27748a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = 'resnet18_imagenet.tflite'\n",
    "\n",
    "NUM_CLASS = 1000 # 指定类别个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c2defb07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2023-08-04 22:17:58,692525][/tmp/ipykernel_10599/1686280459.py:1 <module>][DEBUG]:=======init\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(id=1, result='load model ok!', error=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aidlite = aidlite_gpu.aidlite()\n",
    "\n",
    "# 模型路径 输入维度 输出维度 线程数 是否开启NNAPI\n",
    "# https://docs.aidlux.com/#/intro/ai/ai-aidlite?id=_4fast_annmodel\n",
    "aidlite.FAST_ANNModel(model_path, [256*256*3*4], [NUM_CLASS*4], 3, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d2d24c6",
   "metadata": {},
   "source": [
    "## 初始化摄像头"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f1e92d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 摄像头ID 0-后置 1-前置\n",
    "Camera_ID = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "426d2378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('app runs on port:', 34590)\n",
      "open the cam:0 ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "remi.server      INFO     Started httpserver http://0.0.0.0:34590/\n"
     ]
    }
   ],
   "source": [
    "cap = cvs.VideoCapture(Camera_ID)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4915120",
   "metadata": {},
   "source": [
    "## 逐帧处理函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a252f2f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "remi.request     INFO     built UI (path=/)\n",
      "remi.server.ws   INFO     connection established: ('192.168.1.4', 62057)\n",
      "remi.server.ws   INFO     handshake complete\n",
      "remi.server.ws   INFO     connection established: ('127.0.0.1', 55734)\n",
      "remi.server.ws   INFO     handshake complete\n",
      "remi.server.ws   ERROR    Error managing incoming websocket message\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 162, in read_next_message\n",
      "    length = self.rfile.read(2)\n",
      "  File \"/usr/lib/python3.7/socket.py\", line 589, in readinto\n",
      "    return self._sock.recv_into(b)\n",
      "ConnectionResetError: [Errno 104] Connection reset by peer\n",
      "remi.server.ws   INFO     connection established: ('127.0.0.1', 55768)\n",
      "remi.server.ws   INFO     handshake complete\n"
     ]
    }
   ],
   "source": [
    "def process_frame(img_bgr):\n",
    "    \n",
    "    # 记录该帧开始处理的时间\n",
    "    start_time = time.time()\n",
    "    \n",
    "    ## 预处理\n",
    "    img_tensor = cv2.resize(img_bgr, (256, 256)) # 尺寸缩放\n",
    "    mean = (0.485, 0.456, 0.406) # 三通道的均值\n",
    "    std = (0.229, 0.224, 0.225) # 三通道的标准差\n",
    "    img_tensor = ((img_tensor / 255) - mean) / std\n",
    "    img_tensor = img_tensor.astype('float32')\n",
    "    \n",
    "    ## 推理预测\n",
    "    aidlite.setInput_Float32(img_tensor) # 装填数据\n",
    "    aidlite.invoke() # 推理预测\n",
    "    result = aidlite.getOutput_Float32() # 获得推理预测结果\n",
    "    \n",
    "    ## 解析预测结果\n",
    "    pred_id = np.argmax(result) # 置信度最高类别 ID\n",
    "    pred_class = idx_to_labels[pred_id] # 置信度最高类别名称\n",
    "    \n",
    "    # 用 pillow写中文\n",
    "    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) # BGR转RGB\n",
    "    img_pil = Image.fromarray(img_rgb) # array 转 PIL\n",
    "    draw = ImageDraw.Draw(img_pil)\n",
    "    draw.text((50, 150), pred_class, font=font, fill=(0, 0, 255, 1))\n",
    "    img_rgb = np.array(img_pil) # PIL 转 array\n",
    "    img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR) # RGB转BGR\n",
    "    \n",
    "    # 记录该帧处理完毕的时间\n",
    "    end_time = time.time()\n",
    "    # 计算每秒处理图像帧数FPS\n",
    "    FPS = 1/(end_time - start_time)\n",
    "    # 在画面上写字：图片，字符串，左上角坐标，字体，字体大小，颜色，字体粗细\n",
    "    FPS_string = 'FPS {:.2f}'.format(FPS) # 写在画面上的字符串\n",
    "    img_output = cv2.putText(img_bgr, FPS_string, (50, 120), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n",
    "    \n",
    "    return img_output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf07596",
   "metadata": {},
   "source": [
    "## 预测摄像头单帧画面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a4c1352",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_bgr = cap.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc7673a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_bgr = process_frame(img_bgr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc677fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "remi.request     ERROR    error processing GET request\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 615, in do_GET\n",
      "    self._instance()\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 417, in _instance\n",
      "    self.page.children['head'].set_internal_js(str(id(self)), net_interface_ip, pending_messages_queue_length, websocket_timeout_timer_ms)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/gui.py\", line 1629, in set_internal_js\n",
      "    'event_name':'onerror'})\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/gui.py\", line 470, in add_child\n",
      "    self._render_children_list.remove(key)\n",
      "ValueError: list.remove(x): x not in list\n",
      "remi.server.ws   ERROR    Error managing incoming websocket message\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 166, in read_next_message\n",
      "    length = self.bytetonum(length[1]) & 127\n",
      "IndexError: index out of range\n",
      "remi.request     ERROR    sending websocket spontaneous message\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 497, in _send_spontaneous_websocket_message\n",
      "    if ws.send_message(message):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 209, in send_message\n",
      "    self.request.sendall(out)\n",
      "OSError: [Errno 9] Bad file descriptor\n",
      "remi.server.ws   ERROR    Error managing incoming websocket message\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 166, in read_next_message\n",
      "    length = self.bytetonum(length[1]) & 127\n",
      "IndexError: index out of range\n"
     ]
    }
   ],
   "source": [
    "plt.imshow(img_bgr[:,:,::-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02653e7c",
   "metadata": {},
   "source": [
    "## 逐帧实时处理手机摄像头拍摄的画面-代码模板"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "563852a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    img_bgr = cap.read()\n",
    "    \n",
    "    if img_bgr is None:\n",
    "        continue\n",
    "        \n",
    "    img_bgr = process_frame(img_bgr)\n",
    "    \n",
    "    cvs.imshow(img_bgr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d69ca6",
   "metadata": {},
   "source": [
    "> 点击`重启kernel`关闭摄像头"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6414fc8b",
   "metadata": {},
   "source": [
    "## 视频逐帧处理（模板）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d173880",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "\n",
    "# 视频逐帧处理代码模板\n",
    "# 不需修改任何代码，只需定义process_frame函数即可\n",
    "# 同济子豪兄 2021-7-10\n",
    "\n",
    "def generate_video(input_path='videos/robot.mp4'):\n",
    "    filehead = input_path.split('/')[-1]\n",
    "    output_path = \"out-\" + filehead\n",
    "    \n",
    "    print('视频开始处理',input_path)\n",
    "    \n",
    "    # 获取视频总帧数\n",
    "    cap = cv2.VideoCapture(input_path)\n",
    "    frame_count = 0\n",
    "    while(cap.isOpened()):\n",
    "        success, frame = cap.read()\n",
    "        frame_count += 1\n",
    "        if not success:\n",
    "            break\n",
    "    cap.release()\n",
    "    print('视频总帧数为',frame_count)\n",
    "    \n",
    "    # cv2.namedWindow('Crack Detection and Measurement Video Processing')\n",
    "    cap = cv2.VideoCapture(input_path)\n",
    "    frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "    # fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))\n",
    "    # fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
    "    fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
    "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "\n",
    "    out = cv2.VideoWriter(output_path, fourcc, fps, (int(frame_size[0]), int(frame_size[1])))\n",
    "    \n",
    "    # 进度条绑定视频总帧数\n",
    "    with tqdm(total=frame_count-1) as pbar:\n",
    "        try:\n",
    "            while(cap.isOpened()):\n",
    "                success, frame = cap.read()\n",
    "                if not success:\n",
    "                    break\n",
    "\n",
    "                # 处理帧\n",
    "                # frame_path = './temp_frame.png'\n",
    "                # cv2.imwrite(frame_path, frame)\n",
    "                try:\n",
    "                    frame = process_frame(frame)\n",
    "                except:\n",
    "                    print('报错！', error)\n",
    "                    pass\n",
    "                \n",
    "                if success == True:\n",
    "                    # cv2.imshow('Video Processing', frame)\n",
    "                    out.write(frame)\n",
    "\n",
    "                    # 进度条更新一帧\n",
    "                    pbar.update(1)\n",
    "\n",
    "                # if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "                    # break\n",
    "        except:\n",
    "            print('中途中断')\n",
    "            pass\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "    out.release()\n",
    "    cap.release()\n",
    "    print('视频已保存', output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00ab603a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "视频开始处理 video_4.mp4\n",
      "视频总帧数为 283\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|█████████████████████████████████████████████████████████████████████████████████████████▌                  | 234/282 [00:50<00:11,  4.29it/s]remi.server.ws   ERROR    Error managing incoming websocket message\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/remi/server.py\", line 166, in read_next_message\n",
      "    length = self.bytetonum(length[1]) & 127\n",
      "IndexError: index out of range\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 282/282 [01:00<00:00,  4.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "视频已保存 out-video_4.mp4\n"
     ]
    }
   ],
   "source": [
    "generate_video(input_path='video_4.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e88924",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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